Compression of computed tomography (CT) projection data reduces CT scanner bandwidth and storage costs. Since
fixed-rate compression guarantees predictable bandwidth, fixed-rate compression is preferable to lossless compression,
but fixed-rate compression can introduce image artifacts. This research demonstrates clinically acceptable image quality
at 3:1 compression as judged by a radiologist and as estimated by an image quality metric called local structural
similarity (SSIM). We examine other common, quantitative image quality metrics from image processing, including
peak signal-to-noise (PSNR), contrast-to-noise ratio (CNR), and difference image statistics to quantify the magnitude
and location of image artifacts caused by fixed-rate compression of CT projection data. Masking effects caused by local
contrast, air and bone pixels, and image reconstruction effects at the image's periphery and iso-center explain why
artifacts introduced by compression are not noticed by radiologists. SSIM metrics in this study nearly always exceeds
0.98 (even at 4:1 compression ratios), which is considered visually indistinguishable. The excellent correlation of local
SSIM and subjective image quality assessment confirms that fixed-rate 3:1 projection data compression on CT images
does not affect clinical diagnosis and is rarely noticed. Local SSIM metrics can be used to significantly reduce the
number of viewed images in medical image quality studies.
Compression of computed tomography (CT) projection samples reduces slip ring and disk drive costs. A lowcomplexity,
CT-optimized compression algorithm called Prism CTTM achieves at least 1.59:1 and up to 2.75:1 lossless
compression on twenty-six CT projection data sets. We compare the lossless compression performance of Prism CT to
alternative lossless coders, including Lempel-Ziv, Golomb-Rice, and Huffman coders using representative CT data sets.
Prism CT provides the best mean lossless compression ratio of 1.95:1 on the representative data set. Prism CT
compression can be integrated into existing slip rings using a single FPGA. Prism CT decompression operates at 100
Msamp/sec using one core of a dual-core Xeon CPU. We describe a methodology to evaluate the effects of lossy
compression on image quality to achieve even higher compression ratios. We conclude that lossless compression of raw
CT signals provides significant cost savings and performance improvements for slip rings and disk drive subsystems in
all CT machines. Lossy compression should be considered in future CT data acquisition subsystems because it provides
even more system benefits above lossless compression while achieving transparent diagnostic image quality. This result
is demonstrated on a limited dataset using appropriately selected compression ratios and an experienced radiologist.
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